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Abstract Recapitulating the natural extracellular physical microenvironment has emerged as a promising method for tissue regeneration, as multiple physical interventions, including ultrasound, thermal and electrical therapy, have shown great potential. However, simultaneous coupling of multiple physical cues to highly bio-mimick natural characteristics for improved tissue regeneration still remains formidable. Coupling of intrinsic electrical and mechanical cues has been regarded as an effective way to modulate tissue repair. Nevertheless, precise and convenient manipulation on coupling of mechano-electrical signals within extracellular environment to facilitate tissue regeneration remains challengeable. Herein, a photothermal-sensitive piezoelectric membrane was designed for simultaneous integration of electrical and mechanical signals in response to NIR irradiation. The high-performance mechano-electrical coupling under NIR exposure synergistically triggered the promotion of osteogenic differentiation of stem cells and enhances bone defect regeneration by increasing cellular mechanical sensing, attachment, spreading and cytoskeleton remodeling. This study highlights the coupling of mechanical signals and electrical cues for modulation of osteogenesis, and sheds light on alternative bone tissue engineering therapies with multiple integrated physical cues for tissue repair. Graphical Abstract

The regeneration of maxillofacial bone defects associated with diabetes mellitus remains challenging due to the occlusal loading and hyperglycemia microenvironment. Herein, we propose a material-structure-driven strategy through the additive manufacturing of degradable Zn–Mg–Cu gradient scaffolds. The in situ alloying of Mg and Cu endows Zn alloy with admirable compressive strength for mechanical support and uniform degradation mode for preventing localized rupture. The scaffolds manifest favorable antibacterial, angiogenic, and osteogenic modulation capacity in mimicked hyperglycemic microenvironment, and Mg and Cu promote osteogenic differentiation in the early and late stages, respectively. In addition, the scaffolds expedite diabetic maxillofacial bone ingrowth and regeneration by combining the metabolic regulation effect of divalent metal cations and the hyperboloid and suitable permeability of the gradient structure. RNA sequencing further reveals that RAC1 might be involved in bone formation by regulating the transport and uptake of glucose related to GLUT1 in osteoblasts, contributing to cell function recovery. Inspired by bone healing and structural cues, this study offers an essential understanding of the designation and underlying mechanisms of the material-structure-driven strategy for diabetic maxillofacial bone regeneration.

With the continuous increase of depth in underground engineering, rock bursts or rock explosions can cause instantaneous shock load and result in rock engineering disasters. To solve these problems, a novel high strength and high toughness (HSHT) bolt steel with constant resistance and large deformation is proposed. This study aims to investigate the dynamic constitutive models and microstructure of HSHT steel under extreme loading conditions. A series of dynamic tension tests were conducted using a Split Hopkinson Tensile Bar (SHTB) system with a high-speed camera. The results revealed that the HSHT steel exhibited a positive strain rate effect. As the strain rate increased from 1000 s−1 to 5000 s−1, the yield strength and ultimate strength of the HSHT steel increased gradually. However, no obvious changes in the uniform elongation. Specifically, the yield strength, tension strength, and uniform elongation of strain rate at 5000 s−1 were 1100 MPa, 1170 MPa, and 30%, respectively. According to the results of the SHTB tests, a modified Johnson-Cook model was proposed. Additionally, we investigated the microstructure of HSHT steel at a high strain rate through microscopic characterization techniques. A large number of fine and deep dimples were observed near the fracture of HSHT steel under dynamic loading. Moreover, the HSHT steel demonstrates a significant presence of nano-twins, which effectively impedes crack propagation both within the grain interior and along grain boundaries. The above findings reveal the relationship between mechanical behavior and microstructure in HSHT steel within dynamic loading conditions, providing valuable insights for the design of HSHT steel in underground engineering.

Recently, medium and high-entropy alloys with heterogeneous features have exhibited excellent synergistic effects of strength and toughness. In this work, we have produced a series of cast CrCoNi medium entropy alloys with various heterogeneous microstructures through Al and Ti elements micro-alloying. The heterogeneous microstructure and deformation mechanism are systematically analyzed. The result showed that Al/Ti ratio ranging from 0.5 to 2 has a significant influence on the microstructure and mechanical properties of the CrCoNi-based alloy. The present findings provided efficient guidance for developing of microstructure tailoring method in high and medium-entropy alloys.

Metal anodes (e.g., Li and Zn) are promising candidates for high-energy and high-power rechargeable batteries. However, the commercialization of metal anodes is hampered by irregular dendrite growth, which severely deteriorates the safety and cyclability of metal anodes. Optimizing the electrolyte by nanostructured additives to regulate the metal deposition shows great potential since the electrochemically nonreactive feature endows the regulation function with good sustainability. In this manuscript, the fundamental dendrite formation models and key parameters for stabilizing metal anode are first discussed. The progress and functional mechanism of nanostructured additives for regulating the metal deposition are summarized in terms of regulatory model, i.e., deposition-, adsorption- and dispersion-type. Finally, we also provide a detailed concluding outlook, pointing out the future trend of selecting new nanostructured additive candidates and elucidating synergistic effects and underlying mechanisms with the key attention being given to the assessments of practicality.

Background: Orthostatic intolerance (OI) is the inability to tolerate orthostatic stress during any postural change. The etiology of OI varies, and methods to obtain a specific diagnosis and plan appropriate treatment are important. The tools available within the Chinese context to swiftly identify orthostatic intolerance syndrome (OIS) are currently limited. Methods: Patients with OI symptoms were included in this study and categorized into two groups based on the results of the supine-to-stand test. Those with abnormal test results were assigned to the OIS group, while those with normal test results were placed in the non-OIS group. We evaluated the internal consistency and predictive value of the Chinese Orthostatic Discriminant and Severity Scale (ODSS) by comparing patients’ scores with their physiological measurements collected during orthostatic stress tests and the results of other available questionnaires, including the orthostatic Symptom Questionnaire and Orthostatic Grading Scale (OGS). Results: Patients with OIS scored significantly higher on all three questionnaires and showed significant differences in autonomic responses during orthostatic stress tests compared with non-OIS patients. Receiver operating characteristic curve analysis showed that the orthostatic score from the ODSS had moderate predictive value for the supine test (area under the curve [AUC] = 0.754). Further subgroup analysis revealed that the orthostatic score from the ODSS had uniquely high specificity and sensitivity for identifying patients with orthostatic hypotension with abnormal cerebral blood flow (OH–U, AUC = 0.919). Conclusions: We conclude that the Chinese version of the ODSS has sufficient reliability and validity to distinguish patients with OIS and could possibly be used as a diagnostic tool for OH–U patients. Thus, the Chinese ODSS offers a beneficial screening tool for quickly assessing whether patients have OIS that requires further clinical assessment.

Abstract The high curing temperatures required for traditional benzocyclobutene (BCB) materials have posed limitations on their applicability in high‐temperature‐sensitive fields. To address this challenge, our work focuses on the synthesis of a novel tetraphenylethylene (TPE)‐functionalized BCB monomer, TPE–BCB, achieved through the introduction of an ether bond onto the BCB's four‐membered ring via Williamson reaction. TPE–BCB demonstrates remarkable low‐temperature curing properties, characterized by a ring‐opening peak temperature of 190°C, representing a reduction of 60°C compared to conventional BCBs. Fully cured TPE–BCB resins exhibit exceptional dielectric and mechanical properties, coupled with minimal water absorption. Additionally, the incorporation of TPE with aggregation‐induced emission characteristics enhances the resins’ luminescence and photolithographic capabilities. Notably, our TPE–BCB resins achieve impressive photolithography performance with a resolution ratio of up to 10 μm. In contrast to conventional BCB‐functionalized resins, TPE–BCB offers the dual advantage of low‐temperature curing and luminescence. This development marks a significant step in the advancement of low‐temperature curing BCB materials and serves as a pioneering example in the realm of multilayer wafer bonding materials.

Compelling evidence has shown that geomagnetic disturbances in vertical intensity polarization before great earthquakes are promising precursors across diverse rupture conditions. However, the geomagnetic vertical intensity polarization method uses the spectrum of smooth signals, and the anomalous waveforms of seismic electromagnetic radiation, which are basically nonstationary, have not been adequately considered. By combining pulse amplitude analysis and an experimental study of the cumulative frequency of anomalies, we found that the pulse amplitudes before the 2022 Luding M6.8 earthquake show characteristics of multiple synchronous anomalies, with the highest (or higher) values occurring during the analyzed period. Similar synchronous anomalies were observed before the 2021 Yangbi M6.4 earthquake, the 2022 Lushan M6.1 earthquake and the 2022 Malcolm M6.0 earthquake, and these anomalies indicate migration from the periphery toward the epicenters over time. The synchronous changes are in line with the recognition of previous geomagnetic anomalies with characteristics of high values before an earthquake and gradual recovery after the earthquake. Our study suggests that the pulse amplitude is effective for extracting anomalies in geomagnetic vertical intensity polarization, especially in the presence of nonstationary signals when utilizing observations from multiple station arrays. Our findings highlight the importance of incorporating pulse amplitude analysis into earthquake prediction research on geomagnetic disturbances.

Abstract Background After neoadjuvant therapy, most of the lymph nodes (LNs) will shrink and disappear in patients with rectal cancer. However, LNs that are still detectable on MRI carry a risk of metastasis. This study aimed to evaluate the performance of the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) criterion (short-axis diameter ≥ 5 mm) in diagnosing malignant LNs in patients with rectal cancer after neoadjuvant therapy, and whether nodal morphological characteristics (including shape, border, signal homogeneity, and enhancement homogeneity) could improve the diagnostic efficiency for LNs ≥ 5 mm. Methods This retrospective study included 90 patients with locally advanced rectal cancer who underwent surgery after neoadjuvant therapy and performed preoperative MRI. Two radiologists independently measured the short-axis diameter of LNs and evaluated the morphological characteristics of LNs ≥ 5 mm in consensus. With a per node comparison with histopathology as the reference standard, a ROC curve was performed to evaluate the diagnostic performance of the size criterion. For categorical variables, either a χ 2 test or Fisher’s exact test was used. Results A total of 298 LNs were evaluated. The AUC for nodal size in determining nodal status was 0.81. With a size cutoff value of 5 mm, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 65.9%, 87.0%, 46.8%, 93.6% and 83.9%, respectively. No significant differences were observed in any of the morphological characteristics between benign and malignant LNs ≥ 5 mm (all P > 0.05). Conclusions The ESGAR criterion demonstrated moderate diagnostic performance in identifying malignant LNs in patients with rectal cancer after neoadjuvant therapy. It was effective in determining the status of LNs

ABSTRACTAqueous zinc-based devices (AZDs) are considered as a promising candidate for next-generation energy storage due to high safety, economic benefit, and environmental benignity. However, the wide-spread application of AZD cathodes still faces challenges such as inferior energy density and cycling life. This manuscript focuses on the key design parameters for AZD cathodes and highlights the diverse functions of well-designed carbon architectures in AZD cathodes. The emerging opportunities are also presented to guide future endeavors towards high-performance AZD cathodes.

Since COVID-19, people have suffered tremendous impacts in all aspects of their lives and work, with subtle changes in their environment preferences. The rural areas, with their natural green space, low density, and leisurely habitat, have played an important role after the pandemic and are widely favored by people. Research on rural environments after COVID-19 has received much attention. In the wake of the pandemic, people's needs for the environment have changed not only in terms of physical space, but also in terms of psychological needs. To address the issue of adaptability and resiliency of the future tourism development of the rural areas, this study takes the real subjective feelings of rural visitors as the evaluation standard, and takes the rural gastronomic tourism environment as the research object. We analyzed a sample of 14,373 images and 324,676 comments in Chinese posted by 3484 visitors on social media to explore whether and how people's preferences for rural environments have changed since the pandemic. Findings revealed significant differences in preference for the rural gastronomic tourism environment factors before and after the pandemic. There is variability in environment preferences depending on different gender, environment flexibility and the region. From the perspective of the rural gastronomic tourism environment, the research results provide suggestions for rural planning and rural tourism sustainability, and provide feasible paths for sustainable development and conservation of rural landscapes oriented to human needs, to enhance the resilience and sustainability of rural environments in the future.

In this study, we investigated the effects of salinity on elemental sulfur-driven autotrophic denitrification (SAD) efficiency, and microbial communities. The results revealed that when the salinity was ≤6 g/L, the nitrate removal efficiency in SAD increased with the increasing salinity reaching 95.53% at 6 g/L salinity. Above this salt concentration, the performance of SAD gradually decreased, and the nitrate removal efficiency decreased to 33.63% at 25 g/L salinity. Approximately 5 mg/L of the hazardous nitrite was detectable at 15 g/L salinity, but decreased at 25 g/L salinity, accompanied by the generation of ammonium. When the salinity was ≥15 g/L, the abundance of the salt-tolerant microorganisms, Thiobacillus and Sulfurimonas, increased, while that of other microbial species decreased. This study provides support for the practical application of elemental sulfur-driven autotrophic denitrification in saline nitrate wastewater.

In this paper, a new hybrid permanent magnet variable leakage flux (VLF-HPM) motor is proposed. In order to enhance motor speed range, short arc-shaped and elliptical-shaped magnetic barriers are placed on the q-axis magnet circuit, realizing the so-called leakage flux effect. In addition, ferrite PMs are artfully designed, so self-leakage flux can be reduced while main flux is enhanced. The relationship among PM topology, flux barrier and flux-regulation capability are also investigated in detail. Finally, the electromagnetic performance of VLF-HPM motor is evaluated by the finite element analysis and compared with a conventional V-typed PM motor. It is noted that, compared with conventional V-typed PM motor, the VLF-HPM motor possesses lower core losses and a wider speed range, which offers a new research path for high-performance PM motor.

Oxide inclusions in Ni-based superalloys play a crucial role in determining their mechanical properties, oxidation resistance, and corrosion resistance at high temperatures. In this paper, the source and formation mechanism of different types of oxide inclusions in a high-aluminum Ni-based superalloy were systematically studied. An automatic field emission scanning electron microscope equipped with an energy dispersive spectrometer and a self-designed superalloy inclusion analysis standard was utilized to quantitatively reveal the oxide inclusion characteristics of the high-aluminum Ni-based superalloy prepared via vacuum induction melting (VIM) and vacuum induction remelting (VIR) processes. The experimental results indicate that the typical oxide inclusions in the Ni-based superalloy before the VIR process are irregular MgO·Al2O3 inclusions with sizes of less than 2 μm. After the VIR process, the typical oxide inclusions in the Ni-based superalloy are also MgO·Al2O3 inclusions. However, these oxide inclusions can be classified into three categories: (i) endogenous irregular MgO·Al2O3 inclusions, less than 4.3 μm in size, inherited from the master alloy; (ii) several hundred-micron film-like MgO·Al2O3 inclusions generated as interface reaction products between the MgO crucible and melts; and (iii) millimeter-scale MgO·Al2O3 inclusions and several tens of microns of MgO inclusions from the exfoliation of the MgO crucible matrix.

Climate change and intensive anthropogenic activities have severely challenged the water quality of China’s Pearl River Estuary (PRE). Further investigations into long-term water quality variation and associated driving mechanisms are therefore necessary to support the sustainable development of the PRE’s Greater Bay Area (GBA). This study used remote sensing retrieval to address long-term spatiotemporal chlorophyll-a (Chl-a) variation characteristics in the PRE and the relationship between Chl-a concentrations and socioeconomic/environmental indicators. Three decades of Landsat satellite images and measured data were collected, and a two-band global algorithm was used to retrieve Chl-a concentration data. Results reveal significant spatiotemporal variability in Chl-a concentrations. The space-averaged Chl-a concentration exhibited a slight downward trend during the past three decades, and the multi-year mean value was 5.20 mg/L. Changes to environmental protection policies in recent years have improved overall PRE water quality. The western section of the PRE had the highest Chl-a concentration (i.e., 5.92 mg/L average) while the eastern section had the lowest (i.e., 3.98 mg/L average). This discrepancy was likely caused by the western section’s more intensive industrial activities, resulting in a higher overall wastewater discharge volume. Affected by climatic conditions, winter Chl-a concentrations were evenly distributed while summer concentrations were significantly higher. Additionally, Chl-a concentrations significantly and positively correlated with total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3-N), and the biotic oxygen demand (BOD5). Chl-a concentrations also correlated with external factors (i.e., climate and anthropogenic activities). Among these factors, industrial wastewater discharge and the proportion of primary industries in coastal cities significantly and positively correlated with water quality. This study is intended to help direct water quality improvement management and urban sustainable development in the GBA.

The normal operation of spaceborne parabolic cylindrical reflector antennas under various operating conditions relies on maintaining the root mean square (RMS) of the reflector surface’s deformation within reasonable limits. In engineering practice, the designing of reinforced ribs is the primary way to control the RMS of the reflector surface. However, the layout and dimensions of reinforced ribs for many existing designs rely on the experience of the designer and lack a theoretical foundation. This leads to suboptimal layouts and dimensions in many designs, deviating from the optimal design. To address these concerns, this study proposes a comprehensive design approach that combines both topology optimization and parametric analysis. Optimization and parametric analysis were conducted for a large-sized spaceborne composite parabolic cylindrical reflector antenna. The layout and dimensions of the reinforced ribs were reconstructed based on the optimization results and parametric analysis. This study also obtained the influence of the height and thickness of the reinforced ribs on the RMS of the reflector surface. Subsequently, utilizing antenna temperature field simulations as thermal excitation inputs, finite element thermal distortion analyses were conducted for the reflector surfaces without reinforced ribs, with the original reinforced ribs designed based on empirical methods, and with optimized reinforced ribs. In comparison to the original design of the reinforced ribs, the optimized design, without an increase in the volume of the reinforced ribs, reduced the RMS of the reflector surface from 0.6025 mm to 0.5561 mm, resulting in an optimization ratio of 7.7%. Moreover, when compared to the reflector surface without reinforced ribs, the optimized design achieved a 17.9% reduction in RMS.

ATP-binding cassette (ABC) transporters, acting as cellular “pumps,” facilitate solute translocation through membranes via ATP hydrolysis. Their overexpression is closely tied to multidrug resistance (MDR), a major obstacle in chemotherapy and neurological disorder treatment, hampering drug accumulation and delivery. Extensive research has delved into the intricate interplay between ABC transporter structure, function, and potential inhibition for MDR reversal. Cryo-electron microscopy has been instrumental in unveiling structural details of various MDR-causing ABC transporters, encompassing ABCB1, ABCC1, and ABCG2, as well as the recently revealed ABCC3 and ABCC4 structures. The newly obtained structural insight has deepened our understanding of substrate and drug binding, translocation mechanisms, and inhibitor interactions. Given the growing body of structural information available for human MDR transporters and their associated mechanisms, we believe it is timely to compile a comprehensive review of these transporters and compare their functional mechanisms in the context of multidrug resistance. Therefore, this review primarily focuses on the structural aspects of clinically significant human ABC transporters linked to MDR, with the aim of providing valuable insights to enhance the effectiveness of MDR reversal strategies in clinical therapies.

The Loess Plateau is an important grain-producing area and energy base in China and is an area featuring dramatic changes in both surface and underground processes. However, the associations between surface deformation and groundwater storage changes in different landscape types in the region are still unclear. Based on Sentinel-1 and GRACE (Gravity Recovery and Climate Experiment) data, this study monitored and verified the surface deformation and groundwater storage changes in different landscape types, such as those of the Kubuqi Desert, Hetao Irrigation District, Jinbei Mining Area, and Shendong Mining Area, in the Loess Plateau of China from 2020 to 2021. Through time series and cumulative analysis using the same spatial and temporal resolution, the associations between these two changes in different regions are discussed. The results show that: (1) the surface deformation rates in different landscape types differ significantly. The minimum surface deformation rate in the Kubuqi Desert is −5~5 mm/yr, while the surface deformation rates in the Hetao Irrigation District, the open-pit mine recovery area in the Jinbei Mining Area, and the Shendong Mining Area are −60~25 mm/yr, −25~25 mm/yr, and −95.33~26 mm/yr, respectively. (2) The regional groundwater reserves all showed a decreasing trend, with the Kubuqi Desert, Hetao Irrigation District, Jinbei Mining Area, and Shendong Mining Area declining by 359.42 mm, 103.30 mm, 45.60 mm, and 691.72 mm, respectively. (3) The surface elasticity deformation had the same trend as the temporal fluctuation in groundwater storage, and the diversion activity was the main reason why the temporal surface deformation in the Hetao Irrigation District lagged behind the change in groundwater storage by 1~2 months. The measure of “underground water reservoirs in coal mines” slows down the rate of collapse of coal mine roof formations, resulting in the strongest time-series correlation between mild deformation of the surface of the Shendong mine and changes in the amount of groundwater reserves (R = 0.73). This study analyzes the associations between surface deformation and groundwater storage changes in different landscape areas of the Loess Plateau of China and provides new approaches to analyzing the dynamic associations between the two and the causes of changes in both variables.

In this paper, coupled conformable fractional Fokas-Lenells equation with spatio-temporal dispersion is introduced, which is usually used to simulate the propagation model of pulses’ temporal evolution in birefringent fibers. Firstly, the traveling wave transformation is applied to convert the conformable time–space fractional Fokas-Lenells model into two-dimensional planar dynamic system. Secondly, the qualitative analysis of the coupled conformable fractional Fokas-Lenells equation with spatio-temporal dispersion is discussed by using the plane dynamics system. Moreover, two-dimensional phase portraits, three-dimensional phase portraits, Poincaré sections and sensitivity analysis of its perturbed system are drawn by using the Maple mathematical software. Finally, the optical soliton solutions of the coupled conformable fractional Fokas-Lenells equation with spatio-temporal dispersion are obtained by using planar dynamical system method.

Land surface temperature (LST) is an important parameter affecting ground-air energy exchange, and can be applied in many fields such as drought monitoring, evapotranspiration estimation, and crop yield assessment. The single-channel (SC) algorithm only needs thermal infrared (TIR) data with only one channel to retrieve the LST, but requires accurate atmospheric correction, which is difficult to guarantee in many cases. The split-window (SW) algorithm exploits the difference in atmospheric absorption of adjacent channels and can accurately obtain the LST without atmospheric parameters, but the input requirement of two TIR channels makes the application limited. Some previous studies found that there is a strong correlation between the top-of-atmosphere thermal radiance in adjacient TIR channels with similar wavelength ranges, which makes it possible to decompose a single TIR channel into two TIR channels with close wavelengths using empirical relationships with errors smaller than those caused by the uncertainty of atmospheric correction. In this article, Landsat-7 ETM+ TIR data was decomposed into two virtual TIR channels by fusing with the simultaneously observed Terra moderate resolution imaging spectroradiometer (MODIS) data, and the channel decomposed SW (TCD-SW) based on the generalized nonlinear SW algorithm was developed and applied to retrieve the LST to eliminate the dependence on atmospheric parameters. The validation results using the simulation dataset, ground-measured site data, and well-validated MODIS sea surface temperature product showed that the proposed TCD-SW algorithm achieved more accurate results than the SC algorithm, being more advantageous in the humid atmosphere. The TCD-SW algorithm can be used as a potential new method for LST retrieval from remote sensing data with only one TIR channel under complex atmospheric conditions.

Land surface temperature (LST) is one of important parameter in the earth energy and water budget. Recently, open-source modules or packages are emerging as all-in-one solutions to speed up the rapid development and critical application of LST from thermal infrared (TIR) remote sensing data. In this article, we developed a python package, dubbed Peking University land surface temperature (PKULAST) model to address the retrieval of LST from TIR remote sensing data with different LST retrieval algorithms, such as single channel algorithm, split window algorithm, and the temperature and emissivity separation algorithm. extendable data structures in the model covers common conceptual models used in LST algorithms implementation, such as spectral response functions, surface spectral libraries (ASTER, ECOSTRESS etc.), atmospheric profiles (NCEP, ERA5, MERRA-2, etc.), making it a convenience to construct the processing chain of developing new LST retrieval algorithms and generating LST products. The ASTER imageries are tested to assess the performance of the PKULAST to derive accurate LST. The LST result is then evaluated using long-term ground-based longwave radiation observations collected at seven sites from 2000 to 2021 under cloud-free scenes, and the result shows that the PKULAST model can obtain LST with an uncertainty of 2.7 K in bias, and 4.4 K in root-mean-square error. With the help of predefined extendable data structures and workflows for different sensors, atmospheric profile libraries and surface spectral libraries, new algorithms for retrieving LST can be developed easily. The PKULAST promises to be a tool that seamlessly interoperates with developing LST retrieval algorithms and generating LST scientific products. The code will be released at https://github.com/tirzhu/pkulast.

In order to realize the low vibration design, variable leakage flux permanent magnet (VLF-PM) motor is investigated and purposefully optimized. Firstly, the variable flux characteristics of the VLF-PM motor are studied in detail. Then the electromagnetic vibration characteristics of the VLF-PM motor under different operating conditions are analyzed. On this basis, key electromagnetic force harmonics caused by the vibration at typical operating conditions and torque performances are selected to be optimized. By the adoption of multi-objective optimization, low radial electromagnetic force (REF) and high output torque can be achieved. Both simulation and experimental results verify the effectiveness of the reduced vibration design of LCF-PM motors, which provides a potential research path for the low vibration design of PM motors, especially multi-mode PM motors.

Abstract Bacterial keratitis can lead to intraocular infection and even blindness without prompt and potent treatments. Currently, clinical abuse of antibiotics encouraged the evolution of resistant bacteria. Conventional antibiotic eye drops based keratitis treatment has been heavily restricted due to the lack of bactericidal efficiency and easy induction of bacterial resistance. Hence, developing an effective treatment strategy for bacterial keratitis is of great significance. In this work, we investigated ε‐poly‐l‐lysine (EPL)‐modified polydopamine (PDA) nanoparticles (EPL@PDA NPs)‐mediated antibacterial photothermal therapy (aPTT), to cope with methicillin‐resistant Staphylococcus aureus (MRSA)‐induced keratitis. The surface modification of cationic peptide EPL enables EPL@PDA NPs to specifically target negatively charged MRSA and induces local hyperthermia to kill the bacteria under low ambient temperature. Under near‐infrared (NIR) irradiation, the sterilization efficiency of EPL@PDA NPs suspension for MRSA in vitro was up to 99.96%. The EPL@PDA‐mediated aPTT presented potent antibacterial efficacy in treating MRSA‐induced keratitis with little corneal epithelial cytotoxicity and good biocompatibility. In conclusion, the bacterial‐targeting aPTT platform in this work provides a prospective method for the management of MRSA‐induced refractory bacterial keratitis.

The main purpose of this paper is to discuss the optical soliton solutions and phase portraits of the coupled Sasa–Satsuma model in nonlinear optics. This model is usually used to describe the propagation of femtosecond pulses in optical fibers. By using traveling wave transformation, the coupled Sasa–Satsuma model is simplified into the coupled nonlinear ordinary differential equations. After that, the coupled nonlinear ordinary differential equations are transformed into two-dimensional planar dynamic system with the Hamiltonian system. According to the bifurcation theory of planar dynamical system, the phase portrait of two-dimensional dynamical system is drawn. What is more, some very important optical soliton solutions are also constructed. In order to explain the propagation of optical solitons, three-dimensional diagrams, two-dimensional diagrams and the contour plots of the obtained solutions are drawn by using Maple software.

Geranylgeranylacetone (GGA) exerts cytoprotective activity against various toxic stressors via the thioredoxin (TRX) redox system; however, its effect on skin inflammation and molecular mechanism on inducing the TRX of GGA is still unknown. We investigated the effects of GGA in a murine irritant contact dermatitis (ICD) model induced by croton oil. Both a topical application and oral administration of GGA induced TRX production and Nrf2 activation. GGA ameliorated ear swelling, neutrophil infiltration, and inhibited the expression of TNF-α, IL-1β, GM-CSF, and 8-OHdG. GGA’s cytoprotective effect was stronger orally than topically in mice. In vitro studies also showed that GGA suppressed the expression of NLRP3, TNF-α, IL-1β, and GM-CSF and scavenged ROS in PAM212 cells after phorbol myristate acetate stimulation. Moreover, GGA induced endogenous TRX production and Nrf2 nuclear translocation in PAM212 cells (dependent on the presence of ROS) and activated the PI3K-Akt signaling pathway. GGA significantly downregulated thioredoxin-interacting protein (TXNIP) levels in PAM212 cells treated with or without Nrf2 siRNA. After knocking down Nrf2 in PAM212 cells, the effect of GGA on TRX induction was significantly inhibited. This suggests that GGA suppress ICD by inducing endogenous TRX, which may be regulated by PI3K/Akt/Nrf2 mediation of the TRX redox system.

Tectonic extrusion bypassing the eastern Himalayan syntaxis results in a significant increase in regional stress instability and the associated frequent occurrence of earthquakes in southern Yunnan, China. However, the stress field, and the relationship between the focal mechanism of earthquakes and stress evolution in southern Yunnan, remain enigmatic. In this paper, using a modified grid point test method, we calculated the focal mechanism of ML ≥ 2.5 earthquakes in southern Yunnan (22–25° N, 100–104° E) from January 2009 to June 2023. Utilizing the solutions of historical earthquake focal mechanisms, we obtained the present-day regional tectonic stress field in southern Yunnan via inversion. The results indicate complex and diverse seismic focal mechanisms, and the main types of earthquakes are strike-slip events, followed by normal fault and reverse fault events. The orientations of the maximum and minimum principal stress axes rotate in a clockwise direction from northeast to southwest. The internal stress orientation distribution of the rhombic Sichuan–Yunnan block in the study area is consistent, and the block boundary zone is the site where stress deflection occurs, and the regional tectonic stress field is influenced by the interaction among different blocks. The distribution of R-value in the Lamping–Simao block gradually increases from north to south, indicating that the compressive stress required for material transport becomes relatively small. Combined with the geological and tectonic background of the study area, our results suggest that the speed of block movement gradually decreases from north to south; the distribution of R-value in the South China block is significantly smaller than that of the interior of the Sichuan–Yunnan rhombus, and the proportion of compressive stresses is larger, indicating a stronger extrusion in this region, which may be related to the fact that the Sichuan–Yunnan rhombus is strongly resisted by the South China block in the east.

Cone beam computed tomography (CBCT) has become an essential tool in modern dentistry, allowing dentists to analyze the relationship between teeth and the surrounding tissues. However, traditional manual analysis can be time-consuming and its accuracy depends on the user’s proficiency. To address these limitations, deep learning (DL) systems have been integrated into CBCT analysis to improve accuracy and efficiency. Numerous DL models have been developed for tasks such as automatic diagnosis, segmentation, classification of teeth, inferior alveolar nerve, bone, airway, and preoperative planning. All research articles summarized were from Pubmed, IEEE, Google Scholar, and Web of Science up to December 2022. Many studies have demonstrated that the application of deep learning technology in CBCT examination in dentistry has achieved significant progress, and its accuracy in radiology image analysis has reached the level of clinicians. However, in some fields, its accuracy still needs to be improved. Furthermore, ethical issues and CBCT device differences may prohibit its extensive use. DL models have the potential to be used clinically as medical decision-making aids. The combination of DL and CBCT can highly reduce the workload of image reading. This review provides an up-to-date overview of the current applications of DL on CBCT images in dentistry, highlighting its potential and suggesting directions for future research.

By not only relying on the initial state but also relying on states before, the principle of a self-retrospect dynamic system has been developed to represent the changes in a system since 1991. Afterward, the periphery theory was established, which studies the boundary of a system. We try to integrate the principle of the self-retrospect system and periphery theory in this study. Thus, a self-retrospect periphery gate model, a new expression of temporal-spatial concept, has been derived to investigate the change of a system and forecast it in physics. Firstly, for the equation with a time difference term that controls the motion of the system, a difference-integral equation can be derived by introducing a retrospect function and applying the inner product, partial integral, and mean value theorem. The principle of constructing and solving the difference-integral equation of the system is referred to as the principle of self-retrospect dynamic systems, and the corresponding mathematical model is called the self-retrospect model. The principle of system self-retrospect has been applied to modeling, calculating, and forecasting in many fields such as meteorology, oceanography, hydrology, market, agriculture, transportation, energy, and so on. Secondly, the periphery is defined as an intermediary that can protect the system and exchange with the environment. It is a part of the system and is adjacent to the environment. It has been applied in many fields since the periphery theory was put forward, such as physics, meteorology, water resources, economy, as well as sports. Thirdly, the concept of periphery gate is embedded into the self-retrospect equation, the self-retrospect gate model has been proposed, and the physical implication of the model is mentioned. The mathematical derivation of the model and its physical explanation are the main points of the study. The applications of the model in physics and meteorology are discussed, for example, the relationship between heavy snowfall and airflow passage in Beijing was studied using synoptic meteorology in detail.

Abstract Aqueous zinc-based batteries (AZBs) attract tremendous attention due to the abundant and rechargeable zinc anode. Nonetheless, the requirement of high energy and power densities raises great challenge for the cathode development. Herein we construct an aqueous zinc ion capacitor possessing an unrivaled combination of high energy and power characteristics by employing a unique dual-ion adsorption mechanism in the cathode side. Through a templating/activating co-assisted carbonization procedure, a routine protein-rich biomass transforms into defect-rich carbon with immense surface area of 3657.5 m2 g−1 and electrochemically active heteroatom content of 8.0 at%. Comprehensive characterization and DFT calculations reveal that the obtained carbon cathode exhibits capacitive charge adsorptions toward both the cations and anions, which regularly occur at the specific sites of heteroatom moieties and lattice defects upon different depths of discharge/charge. The dual-ion adsorption mechanism endows the assembled cells with maximum capacity of 257 mAh g−1 and retention of 72 mAh g−1 at ultrahigh current density of 100 A g−1 (400 C), corresponding to the outstanding energy and power of 168 Wh kg−1 and 61,700 W kg−1. Furthermore, practical battery configurations of solid-state pouch and cable-type cells display excellent reliability in electrochemistry as flexible and knittable power sources.

Spatio-temporally continuous and high-quality soil moisture (SM) is very important for assessing changes in the water cycle and climate, especially over the Tibetan plateau (TP). Data fusion is an important method to improve the quality of SM product. However, limited observation overlaps between different satellite SM products, caused by inherent gaps, make it difficult to fuse them to create a continuous and high-quality product. In this study, an SM spatio-temporal continuity and quality simultaneously improving algorithm is proposed. The first step of the approach is obtaining spatio-temporally continuous reference data, including land surface temperature (LST), normalized difference vegetation index (NDVI), Albedo, and digital elevation model (DEM). The second step is training the general regression neural network (GRNN) model with all available essential climate variables (ECV) and Fengyun (FY) SM. The last step is predicting the spatio-temporally continuous and high-quality SM using the trained GRNN derived by the spatio-temporal continuity reference data. An implementation of the algorithm on the TP showed that, compared with the original ECV and FY SM, both the continuity and quality of the fused SM product were largely improved in terms of coverage (72.5%), correlation (R = 0.809), root mean square error (0.081 cm3 cm-3) and bias (0.050 cm3 cm-3). The algorithm showed a good performance in obtaining spatio-temporal variation fusion weights over the TP. This spatio-temporally continuous and high-quality SM of the TP will help advance our understanding of global and regional changes in water cycle and climate.

Accurate and timely crop yield estimation is critical for food security and sustainable development. The rapid development of unmanned aerial vehicles (UAVs) offers a new approach to acquire high spatio-temporal resolution imagery of farmland at a low cost. In order to realize the full potential of UAV platform and sensor, machine learning has been introduced to estimate crop yield, but the shortages of field measurements have troubled researchers. In this article, the CW-RF model, a new wheat yield estimation model suitable for the North China plain, was established using random forest, and the crop growth model (the CERES-wheat model) was chosen to simulate abundant training samples for random forest at field plot scale. According to CERES-wheat model simulation, the leaf area index (LAI) and leaf nitrogen content (LNC) at the wheat jointing and heading stages were selected as the most sensitive parameters, and were retrieved from UAV hyperspectral imagery using the directional second derivative and angular insensitivity vegetation index methods, respectively. Then the retrieved LAI and LNC results were input into the CW-RF model to estimate winter wheat yield. The field validation in Luohe, Henan showed that the root-mean-squared error of the retrieved LAI and LNC were 6.27% and 12.17% at jointing stages, 9.21% and 13.64% at heading stages, respectively. The RMSE of estimated yield was 1,008.08 kg/ha, and the mean absolute percent error of estimated yield was 9.36%, demonstrating the available of the CW-RF model in wheat yield estimation at field plot scale. Apart from Luohe, validations in some other fields (e.g., Xiaotangshan, Beijing), prove the wide applicability of the CW-RF model. In addition, the UAV hyperspectral data were found to significantly improve the retrieval accuracy, and further improve CW-RF model estimation accuracy. In conclusion, this article showed that the CERES-Wheat model simulation can be important data source for machine learning-based wheat yield estimation model at field plot scale, and the hyperspectral sensor mounted on a UAV is a feasible remote sensing data acquisition mode for winter wheat growth monitoring and yield estimation.

Single-stationed geomagnetic vertical intensity polarization (GVIP) anomalies have demonstrated good predictions of the occurrence of large earthquakes in Japan. Nonetheless, due to the lack of a previously densified geomagnetic network, how the multistationary GVIP anomaly (MGVIPA) corresponds to impending earthquakes remains poorly understood. Based on the newly constructed geomagnetic network from 2014 in Qinghai, China, which is composed of 23 electromagnetic stations, we suggested an MGVIPA method to analyze the correlation with large earthquakes since 2015. The results show that (1) the occurrence of MGVIPA is characterized by clusters in time that continue in a short period; (2) the spatial distribution of MGVIPA usually occurs with high values synchronously at several places over the same period; and (3) the Mw ≥ 6 earthquakes occurred in the regions indicated by MGVIPA within a period ranging from 3 months to 1 year from 2015 to 2021 in Qinghai, China.

An outer-independent Italian dominating function (OIIDF) on a graph G with vertex set V(G) is defined as a function f : V(G) → {0, 1, 2}, such that every vertex v ∈ V(G) with f(v) = 0 has at least two neighbors assigned 1 under f or one neighbor w with f(w) = 2, and the set {u ∈ V | f (u) = 0} is independent. The weight of an OIIDF f is the value w(f) = Σu∈V(G) f(u). The minimum weight of an OIIDF on a graph G is called the outer-independent Italian domination number γoiI(G) of G. In this paper, we initiate the study of the outer-independent Italian domination number and present the bounds on the outer-independent Italian domination number in terms of the order, diameter, and vertex cover number. In addition, we establish the lower and upper bounds on γoiI (T) when T is a tree and characterize all extremal trees constructively. We also give the Nordhaus-Gaddum-type inequalities.

Global climate changes have a great impact on terrestrial ecosystems. Vegetation is an important component of ecosystems, and the impact of climate changes on ecosystems can be determined by studying vegetation phenology. Vegetation phenology refers to the phenomenon of periodic changes in plants, such as germination, flowering and defoliation, with the seasonal change of climate during the annual growth cycle, and it is considered to be one of the most efficient indicators to monitor climate changes. This study collected the global land surface satellite leaf area index (GLASS LAI) products, meteorological data sets and other auxiliary data in the Three-River headwaters region from 2001 to 2018; rebuilt the vegetation LAI annual growth curve by using the asymmetric Gaussian (A-G) fitting method and extracted the three vegetation phenological data (including Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing Season (LOS)) by the maximum slope method. In addition, it also integrated Sen’s trend analysis method and the Mann-Kendall test method to explore the temporal and spatial variation trends of vegetation phenology and explored the relationship between vegetation phenology and meteorological factors through a partial correlation analysis and multiple linear regression models. The results of this study showed that: (1) the SOS of vegetation in the Three-River headwaters region is concentrated between the beginning and the end of May, with an interannual change rate of −0.14 d/a. The EOS of vegetation is concentrated between the beginning and the middle of October, with an interannual change rate of 0.02 d/a. The LOS of vegetation is concentrated between 4 and 5 months, with an interannual change rate of 0.21 d/a. (2) Through the comparison and verification with the vegetation phenological data observed at the stations, it was found that the precision of the vegetation phonology extracted by the A-G method and the maximum slope method based on GLASS LAI data is higher (MAE is 7.6 d, RMSE is 8.4 d) and slightly better than the vegetation phenological data (MAE is 9.9 d, RMSE is 10.9 d) extracted based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) product. (3) The correlation between the SOS of vegetation and the average temperature in March–May is the strongest. The SOS of vegetation is advanced by 1.97 days for every 1 °C increase in the average temperature in March–May; the correlation between the EOS of vegetation and the cumulative sunshine duration in August–October is the strongest. The EOS of vegetation is advanced by 0.07 days for every 10-h increase in the cumulative sunshine duration in August–October.

Based on the daily temperature and precipitation data from 1961 to 2021 obtained from 2019 national meteorological stations of China, and by means of Meteorological Drought Comprehensive Index (MCI) and some improved identification methods, we identified all drought event processes in all the seven regions of China in this paper. We also carefully analyzed these regional droughts and made following conclusions: drought was spreading towards southern China with an increasing frequency; consecutive drought year group occurred in all the seven regions of China with an increasing frequency under the background of global warming; both drought duration and comprehensive intensity consistently changed with the area affected by drought, with a correlation coefficient of 0.52–0.67 and 0.88–0.99 respectively, and passed the significance test of 0.05. The method used in this paper could be employed to effectively monitor and evaluate drought processes from multiple dimensions including duration, comprehensive intensity and area affected by drought. Thus, it actually provided a helpful decision-making basis for governments to prevent the risk of drought.

Abstract The lunar surface temperature (LST) derived from thermal infrared (TIR) measurements can aid in understanding the physical properties of the lunar surface. The Diviner Lunar Radiometer Experiment (herein, Diviner) sensor provides global lunar surface observation in seven TIR channels. However, its retrieval of LST constantly uses a single emissivity value (i.e., 0.95) by ignoring the spatial variation of lunar surface, thereby reducing the accuracy of temperature and day–night temperature difference. To overcome this problem, this study developed a physical method called temperature–emissivity separation (TES) algorithm to retrieve LST and lunar surface emissivity from the daytime observation in three Christiansen Feature (CF) channels (7.55–8.05, 8.10–8.40, and 8.38–8.60 μm) of the Diviner, and then used the emissivity from daytime observation to inverse LST at nighttime observation. Findings showed that the TES algorithm could retrieve LST and emissivity with an error of less than 0.8 K and 0.008, respectively. However, observation noise significantly affected the retrieval accuracy, particularly for the low‐temperature pixels; moreover, high retrieval accuracy requires a surface temperature higher than 240 K. The new algorithm was applied to obtain the daytime and nighttime LST and emissivity from the Diviner images. Results showed that the LST retrieved from the algorithm differed approximately 3.9 K from that calculated from a single emissivity 0.95. Finally, an example of global surface temperature and emissivity were obtained. Consequently, the CF pixels were found to distribute in the latitude range from −60° to 60°; however, they did not have a large distribution in high‐latitude and near‐polar regions.

Due to the interaction and external interference, the crowds will constantly and dynamically adjust their evacuation path in the evacuation process to achieve the purpose of rapid evacuation. The information from previous process can be used to modify the current evacuation control information to achieve a better evacuation effect, and iterative learning control can achieve an effective prediction of the expected path within a limited running time. In order to depict this process, the social force model is improved based on an iterative extended state observer so that the crowds can move along the optimal evacuation path. First, the objective function of the optimal evacuation path is established in the improved model, and an iterative extended state observer is designed to get the estimated value. Second, the above model is verified through simulation experiments. The results show that, as the number of iterations increases, the evacuation time shows a trend of first decreasing and then increasing.

Comprehensive identification of drought events is of great significance for monitoring and evaluating drought processes. Based on the date of daily precipitation, temperature and drought-affected area of 403 meteorological stations in North China from 1960 to 2019, the Comprehensive Drought Process Intensity Index (CDPII) has been developed by using the Meteorological-drought Composite Index (MCI) and regional drought process identification method, as well as the EIDR theory method. The regional drought processes in the past 60 years in North China, including Beijing, Tianjin, Hebei, Shanxi and Middle Inner Mongolia, were analyzed and identified. The result shows that the distribution characteristic of droughts with different intensities is as follows: The number of days of all annual-average mild droughts, moderate droughts and severe droughts was highest in Tianjin and that of extreme droughts was highest in Shanxi. The number of days of mild droughts was highest in May and lowest in January. The number of days of moderate droughts was highest in June. The number of days with mild and moderate drought showed an overall increasing trend, while the number of days with severe drought and above showed an overall decreasing trend (through a 95% significance test). The number of drought days was the highest in the 1990s. The annual frequency of drought is between 66.7% and 86.7%; the drought frequency in Hebei is the highest at 86.7%, followed by Beijing at 80%. There were 75 regional drought processes in North China from 1960 to 2019, and the correlation coefficient between process intensity and the drought-affected area was 0.55, which passed the 99% significance test. The comprehensive intensity of drought process from 27 April to 1 September 1972 was the strongest. From 18 May to 31 October 1965, the drought lasted 167 days. The overall drought intensity had a slight weakening trend in the past 60 years. A total of 75 regional drought processes occurred in North China, and the process intensity showed a trend of wavy decline with a determination coefficient (R2) of 0.079 (95% significance test). Overall, the regional drought process identification method and strength assessment result tally with the drought disaster, which can better identify the regional drought process. Furthermore, including the last days, the average intensity, average scope comprehensive strength, there are many angles to monitor and evaluate the drought and drought process. These provide a reference for drought control and decision-making.

Vegetation biophysical products offer unique opportunities to examine long-term vegetation dynamics and land surface phenology (LSP). It is important to understand the time-series performances of various global biophysical products for global change research. However, few endeavors have been dedicated to assessing the performances of long-term change characteristics or LSP extraction derived from different satellite products, especially in mountainous areas with highly fragmented and rugged surfaces. In this paper, we assessed the time-series characteristics and LSP detections of Global LAnd Surface Satellite (GLASS) leaf area index (LAI), fractional vegetation cover (FVC), and gross primary production (GPP) products across the Three-River Source Region (TRSR). The performances of products’ temporal agreements and their statistical relationship as a function of topographic indices and heterogeneous pixels, respectively, were investigated through intercomparison among three products during the period 2000 to 2018. The results show that the phenological differences between FVC and two other products are beyond 10 days over more than 35% of the pixels in TRSR. The long-term trend of FVC diverges significantly from GPP and LAI for 13.96% of the total pixels, and the percentages of mismatched pixels between FVC and two other products are 33.24% in the correlation comparison. Moreover, good agreements are observed between GPP and LAI, both in terms of LSP and interannual variations. Finally, the LSP and long-term dynamics of the three products exhibit poor performances on heterogeneous surfaces and complex topographic areas, which reflects the potential impacts of environmental factors and algorithmic imperfections on the quality and performances of different products. Our study highlights the spatiotemporal disparities in detections of surface vegetation activity in mountainous areas by using different biophysical products. Future global change studies may require multiple high-quality satellite products with long-term stability as data support.

Since the application of silicon materials in electronic devices in the 1950s, microprocessors are continuously getting smaller, faster, smarter, and larger in data storage capacity. One important factor that makes progress possible is decreasing the dielectric constant of the insulating layer within the integrated circuit (IC). Nevertheless, the evolution of interlayer dielectrics (ILDs) is not driven by a single factor. At first, the objective was to reduce the dielectric constant (k). Reduction of the dielectric constant of a material can be accomplished by selecting chemical bonds with low polarizability and introducing porosity. Moving from silicon dioxide, silsesquioxane-based materials, and silica-based materials to porous silica materials, the industry has been able to reduce the ILDs’ dielectric constant from 4.5 to as low as 1.5. However, porous ILDs are mechanically weak, thermally unstable, and poorly compatible with other materials, which gives them the tendency to absorb chemicals, moisture, etc. All these features create many challenges for the integration of IC during the dual-damascene process, with plasma-induced damage (PID) being the most devastating one. Since the discovery of porous materials, the industry has shifted its focus from decreasing ILDs’ dielectric constant to overcoming these integration challenges. More supplementary precursors (such as Si–C–Si structured compounds), deposition processes (such as NH3 plasma treatment), and post porosity plasma protection treatment (P4) were invented to solve integration-related challenges. Herein, we present the evolution of interlayer dielectric materials driven by the following three aspects, classification of dielectric materials, deposition methods, and key issues encountered and solved during the integration phase. We aim to provide a brief overview of the development of low-k dielectric materials over the past few decades.

Abstract Typhoid and paratyphoid are two common enteric infectious diseases with serious gastrointestinal symptoms. Data was collected of the registered cases in Zhejiang Province from 2005 to 2015. The epidemiological characteristics were investigated and high-risk regions were detected with descriptive epidemiological methods and in-depth spatio-temporal statistics. A sharp decline in the incidences of both diseases was observed. The seasonal patterns were identified with typhoid and paratyphoid, one in summer from May to September was observed from 2005 to 2010 and the other lesser one in spring from January to March only observed from 2005 to 2007. The men were more susceptible and the adults aged 20 to 60 constituted the major infected population. The farmers were more likely to get infected, especially to typhoid. The Wilcoxon sum rank test proved that the incidences in the coastal counties were significantly higher than the inland. Besides, a positive autocorrelation was obtained with typhoid fever in global autocorrelation analysis but not with paratyphoid fever. Local autocorrelation analysis and spatio-temporal scan statistics revealed that high-risk clusters were located mainly in the coastal regions with typhoid fever but scattered across the province with paratyphoid fever. The spatial risks were evaluated quantitatively with hierarchical Bayesian models.

Fault diagnosis of rotating machinery mainly includes fault feature extraction and fault classification. Vibration signal from the operation of machinery usually could help diagnosing the operational state of equipment. Different types of fault usually have different vibrational features, which are actually the basis of fault diagnosis. This paper proposes a novel fault diagnosis model, which extracts features by combining vibration severity, dyadic wavelet energy time-spectrum, and coefficient power spectrum of the maximum wavelet energy level (VWC) at the feature extraction stage. At the stage of fault classification, we design a support vector machine (SVM) based on the modified shuffled frog-leaping algorithm (MSFLA) for the accurate classifying machinery fault method. Specifically, we use the MSFLA method to optimize SVM parameters. MSFLA can avoid getting trapped into local optimum, speeding up convergence, and improving classification accuracy. Finally, we evaluate our model on real rotating machinery platform, which has four different states, i.e., normal state, eccentric axle fault (EAF), bearing pedestal fault (BPF), and sealing ring wear fault (SRWF). As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. Based on the extracted features, we further compare our classification method with other three fault classification methods, i.e., backpropagation neural network (BPNN), artificial chemical reaction optimization algorithm (ACROA-SVM), and SFLA-SVM. The experiment results show that MSFLA-SVM achieves a much higher fault classification rate than BPNN, ACROA-SVM, and SFLA-SVM.

Gross primary productivity (GPP) represents total vegetation productivity and is crucial in regional or global carbon balance. The Northeast China (NEC), abundant in vegetation resources, has a relatively large vegetation productivity; however, under obvious climate change (especially warming), whether and how will the vegetation productivity and ecosystem function of this region changed in a long time period needs to be revealed. With the help of GPP products provided by the Global LAnd Surface Satellite (GLASS) program, this paper gives an overview of the regional feedback of vegetation productivity to the changing climate (including temperature, precipitation, and solar radiation) across the NEC from 1982 to 2015. Analyzing results show a slight positive response of vegetation productivities to warming across the NEC with an overall increasing trend of GPPGS (accumulated GPP within the growing season of each year) at 4.95 g C/m2. yr−2 over the last three decades. More specifically, the growth of crops, rather than forests, contributes more to the total increasing productivity, which is mainly induced by the agricultural technological progress as well as warming. As for GPP in forested area in the NEC, the slight increment of GPPGS in northern, high-latitude forested region of the NEC was caused by warming, while non-significant variation of GPPGS was found in southern, low-latitude forested region. In addition, an obvious greening trend, as reported in other regions, was also found in the NEC, but GPPGS of forests in southern NEC did not have significant variations, which indicated that vegetation productivity is not bound to increase simultaneously with greening, except for these high-latitude forested areas in the NEC. The regional feedback of vegetation productivity to climate change in the NEC can be an indicator for vegetations growing in higher latitudes in the future under continued climate change.

The Three-River Source Region (TRSR) is an important area for the ecological security of China. Vegetation growth has been affected by the climate change, topography, and human activities in this area. However, few studies have focused on analyzing time series tendencies of vegetation change in various terrain conditions. To address this issue in the TRSR, this study explored vegetation stability, tendency, and sustainability with multiple methods (e.g., coefficient of variation, Theil-Sen median trend analysis, Mann-Kendall test, and Hurst index) based on the 2000–2017 Global LAnd Surface Satellite Leaf Area Index (GLASS LAI) product. The differentiation patterns of LAI variations and multiyear mean LAI value under different topographic factors were also investigated in combination with digital elevation model (DEM). The results showed that (1) the mean LAI value in the study area increased, with a linear tendency of 0.013·10 a−1; (2) LAI values decreased from southeast to northwest in terms of spatial distribution and the CV indicated LAI variations were relatively stable; (3) the trend analysis revealed that the improved area of LAI accounted for 62.72% which was larger than the degraded area (37.28%), and hurst index revealed a weak anti-sustaining effect of the current tendencies; and (4) the increasing trend was found in multiyear mean LAI value as relief amplitude and slope increased, while LAI stability improved with increasing slope. They exhibited a clear regular pattern. Moreover, significant improvement in LAI generally occurred in low-altitude and flat areas. Finally, the overall improvement and sustainability of LAI improved when moving from sunny aspects to shady aspects, but the LAI stability decreased. Note that vegetation degradation was observed in some high slope areas and was further aggravated. This study is beneficial for revealing the spatial and temporal changes of LAI and their changing rules as a function of different topographic factors in the TRSR. Meanwhile, the results of this study provide theoretical support for sustainable development of this area.

Defect-rich photocatalytic materials with excellent charge transfer properties are very popular. Herein, Sm-doped CeO2 nanorods were annealed in a N2 atmosphere to obtain the defective Sm-doped CeO2 photocatalysts (Vo–Sm–CeO2). The morphology and structure of Vo–Sm–CeO2 were systematically characterized. The Vo–Sm–CeO2 nanorods demonstrated an excellent photodegradation performance of methyl blue under visible light irradiation compared to CeO2 nanorods and Sm–CeO2. Reactive oxygen species including OH, ·O2−, and h+ were confirmed to play a pivotal role in the removal of pollutants via electron spin resonance spectroscopy. Doping Sm enhances the conductivity of CeO2 nanorods, benefiting photogenerated electrons being removed from the surface reactive sites, resulting in the superior performance.

The original pattern recognition and classification of crop diseases needs to collect a large amount of data in the field and send them next to a computer server through the network for recognition and classification. This method usually takes a long time, is expensive, and is difficult to carry out for timely monitoring of crop diseases, causing delays to diagnosis and treatment. With the emergence of edge computing, one can attempt to deploy the pattern recognition algorithm to the farmland environment and monitor the growth of crops promptly. However, due to the limited resources of the edge device, the original deep recognition model is challenging to apply. Due to this, in this article, a recognition model based on a depthwise separable convolutional neural network (DSCNN) is proposed, which operation particularities include a significant reduction in the number of parameters and the amount of computation, making the proposed design well suited for the edge. To show its effectiveness, simulation results are compared with the main convolution neural network (CNN) models LeNet and Visual Geometry Group Network (VGGNet) and show that, based on high recognition accuracy, the recognition time of the proposed model is reduced by 80.9% and 94.4%, respectively. Given its fast recognition speed and high recognition accuracy, the model is suitable for the real-time monitoring and recognition of crop diseases by provisioning remote embedded equipment and deploying the proposed model using edge computing.

Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d−1, −0.16 mm d−1, and 0.65 mm d−1, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change.

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25−40 km and 2−3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2−3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.